Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation Neural Networks

Rizky Dwi Santoso; Zein Hanni Pradana; Gunawan Wibisono

doi:10.1109/COMNETSAT59769.2023.10420668

Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation Neural Networks

Rizky Dwi Santoso, Zein Hanni Pradana, Gunawan Wibisono

Department of Electrical Engineering

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

Abstract

Intensive research in the field of signal processing has driven remarkable advancements in communication technology, particularly in the realm of voice recognition. Voice recognition concepts find application across various domains, with one such application being sound recognition within the context of Continuous Variable Transmission (CVT) for 110cc motor scooters.This study aims to identify potential issues in CVT systems by employing artificial neural networks using Learning Predictive Coding (LPC), Mel Frequency Cepstral Coefficient (MFCC), the Artificial Neural Network (ANN) Backpropagation method to classify distinct sounds emanating from Honda Scoopy 110cc motor scooters. The dataset used comprises 100 CVT engine sound recordings, equally distributed between 50 samples of normal engine sounds and 50 samples of damaged engine sounds.The research findings reveal the highest level of accuracy achieved with order 16 and 16 hidden neurons, resulting in a testing accuracy of 81.3%, a validation accuracy of 100.00%, and a testing accuracy of 90%. This data strongly supports the effectiveness of the backpropagation artificial neural network method for precise CVT issue identification.

Original language	English
Title of host publication	Proceeding - COMNETSAT 2023
Subtitle of host publication	IEEE International Conference on Communication, Networks and Satellite
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	335-340
Number of pages	6
ISBN (Electronic)	9798350341102
DOIs	https://doi.org/10.1109/COMNETSAT59769.2023.10420668
Publication status	Published - 2023
Event	12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023 - Malang, Indonesia Duration: 23 Nov 2023 → 25 Nov 2023

Publication series

Name	Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite

Conference

Conference	12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023
Country/Territory	Indonesia
City	Malang
Period	23/11/23 → 25/11/23

Keywords

AW
Backpropagation
CVT
LPC
MFCC

Access to Document

10.1109/COMNETSAT59769.2023.10420668

Cite this

Santoso, R. D., Pradana, Z. H., & Wibisono, G. (2023). Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation Neural Networks. In Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite (pp. 335-340). (Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/COMNETSAT59769.2023.10420668

Santoso, Rizky Dwi ; Pradana, Zein Hanni ; Wibisono, Gunawan. / Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation Neural Networks. Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 335-340 (Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite).

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title = "Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation Neural Networks",

abstract = "Intensive research in the field of signal processing has driven remarkable advancements in communication technology, particularly in the realm of voice recognition. Voice recognition concepts find application across various domains, with one such application being sound recognition within the context of Continuous Variable Transmission (CVT) for 110cc motor scooters.This study aims to identify potential issues in CVT systems by employing artificial neural networks using Learning Predictive Coding (LPC), Mel Frequency Cepstral Coefficient (MFCC), the Artificial Neural Network (ANN) Backpropagation method to classify distinct sounds emanating from Honda Scoopy 110cc motor scooters. The dataset used comprises 100 CVT engine sound recordings, equally distributed between 50 samples of normal engine sounds and 50 samples of damaged engine sounds.The research findings reveal the highest level of accuracy achieved with order 16 and 16 hidden neurons, resulting in a testing accuracy of 81.3%, a validation accuracy of 100.00%, and a testing accuracy of 90%. This data strongly supports the effectiveness of the backpropagation artificial neural network method for precise CVT issue identification.",

keywords = "AW, Backpropagation, CVT, LPC, MFCC",

author = "Santoso, {Rizky Dwi} and Pradana, {Zein Hanni} and Gunawan Wibisono",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023 ; Conference date: 23-11-2023 Through 25-11-2023",

year = "2023",

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language = "English",

series = "Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite",

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Santoso, RD, Pradana, ZH & Wibisono, G 2023, Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation Neural Networks. in Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite. Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite, Institute of Electrical and Electronics Engineers Inc., pp. 335-340, 12th IEEE International Conference on Communication, Networks and Satellite, COMNETSAT 2023, Malang, Indonesia, 23/11/23. https://doi.org/10.1109/COMNETSAT59769.2023.10420668

Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation Neural Networks. / Santoso, Rizky Dwi; Pradana, Zein Hanni; Wibisono, Gunawan.
Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite. Institute of Electrical and Electronics Engineers Inc., 2023. p. 335-340 (Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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AU - Pradana, Zein Hanni

AU - Wibisono, Gunawan

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Y1 - 2023

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AB - Intensive research in the field of signal processing has driven remarkable advancements in communication technology, particularly in the realm of voice recognition. Voice recognition concepts find application across various domains, with one such application being sound recognition within the context of Continuous Variable Transmission (CVT) for 110cc motor scooters.This study aims to identify potential issues in CVT systems by employing artificial neural networks using Learning Predictive Coding (LPC), Mel Frequency Cepstral Coefficient (MFCC), the Artificial Neural Network (ANN) Backpropagation method to classify distinct sounds emanating from Honda Scoopy 110cc motor scooters. The dataset used comprises 100 CVT engine sound recordings, equally distributed between 50 samples of normal engine sounds and 50 samples of damaged engine sounds.The research findings reveal the highest level of accuracy achieved with order 16 and 16 hidden neurons, resulting in a testing accuracy of 81.3%, a validation accuracy of 100.00%, and a testing accuracy of 90%. This data strongly supports the effectiveness of the backpropagation artificial neural network method for precise CVT issue identification.

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Santoso RD, Pradana ZH, Wibisono G. Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation Neural Networks. In Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite. Institute of Electrical and Electronics Engineers Inc. 2023. p. 335-340. (Proceeding - COMNETSAT 2023: IEEE International Conference on Communication, Networks and Satellite). doi: 10.1109/COMNETSAT59769.2023.10420668

Fault Identification in Honda Scoopy 110CC Continuous Variable Transmission Using Backpropagation Neural Networks

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Keywords

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